Methodologies for Discovering Sets of Critical Products

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：10

、訪客IP：3.22.98.3

姓名

黃振萬(Chen-Wan Huang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

(Methodologies for Discovering Sets of Critical Products)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

在數位轉型時代，零售業者能夠識別出重要客戶與其相關之關鍵商品來提升公司營運績效至關重要。關鍵商品是重要客戶之關鍵購買項目，但在一般消費客戶群中並非具有影響性。儘管關鍵商品之銷售量可能偏低，但仍建議應持續保留在零售店內貨架上，藉以留住店家之重要客戶。過去文獻中少有研究考慮關鍵商品並找出識別模型。本研究提出一種利用垂直數據庫架構來識別出關鍵商品之改良演算法，並將該演算法應用於實際零售超市之交易數據庫，進行實驗以驗證其有效性。經過三種創新過濾條件設計，Precision rate分別達到80.55%、82.15% 與82.35％。本研究是第一個能夠結合多種過濾方法來發掘零售關鍵商品之研究。

摘要(英)

Identifying critical products and important customers to strengthen company performance is vitally important in the digital transformation era. Critical products are itemsets that are preferred by important customers and yet not popular among ordinary customers. As a result, critical products should be kept on the shelf even if their sales volume is lower than that of other popular products. However, few studies have considered identifying critical products or their potentially valuable customer patterns. Therefore, an innovative algorithm that takes advantage of vertical databases to identify critical products was designed in this study. The proposed algorithm is applied to a transactions database of a midsize supermarket to verify the performance. The result showed that the precision of identifying critical products reached 80.55%, 82.15%, and 82.35% for three different filtering criteria. To the best of our knowledge, this dissertation is the first to use multiple filtering criteria to identify critical products.

關鍵字(中)

★ 關鍵商品
★ 數據挖掘
★ 垂直數據庫
★ 頻繁項目集挖掘
★ RFM

關鍵字(英)

★ critical products
★ data mining
★ vertical database
★ frequent itemsets mining
★ RFM

論文目次

Table of Contents
Chinese Abstract i
English Abstract ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
List of Tables vii
Chapter I Introduction 1
Chapter II Literature review 4
2-1 Retail transaction logs analysis 4
2-2 Infrequent pattern mining 5
2-3 Vertical database mining algorithm 6
2-4 High utility itemsets mining 7
Chapter III Methodology development 9
3-1 Definitions of data structures and criteria 10
3-2 Algorithm of Improved Equivalence Class Transformation 16
Chapter IV Experiments with frequency consideration 21
4-1 Data collection and cleansing 21
4-2 Customers evaluation and segmentation 21
4-3 Experiment result of bipartite segmentation 24
4-4 Experiment result of multiple segmentation 25
4-5 Sensitivity analysis of value N 28
4-6 Sensitivity analysis of interval 28
4-7 Summary 30
Chapter V Enriching methodologies with utility 32
5-1 Definitions of utility and criteria 32
5-2 Proposed IECTu algorithm 35
Chapter VI Experiments with utility consideration 40
6-1 Data collection and utility information 40
6-2 Experiment results 40
Chapter VII Conclusion 44
References 45

參考文獻

[1] T. Vafeiadis, K.I. Diamantaras, G. Sarigiannidis G and K.C. Chatzisavvas, A comparison of machine learning techniques for customer churn prediction, Simulation Modelling Practice and Theory, Vol 55, 2015, pp. 1-9. http://doi.org/10.1016/j.simpat.2015.03.003.
[2] R. Koch, The 80/20 Principle: The Secret to Achieving More with Less, London: Nicholas Brealey, 1998.
[3] A.J. Badgaiyan and A. Verma, Does urge to buy impulsively differ from impulsive buying behaviour? Assessing the impact of situational factors, Journal of Retailing and Consumer Services, Vol 22, 2015, pp. 145-157. http://doi.org/10.1016/j.jretconser.2014.10.002.
[4] S. Atulkar and B. Kesari, Role of consumer traits and situational factors on impulse buying: does gender matter?, International Journal of Retail and Distribution Management, Vol 46 (4), 2018, pp. 386-405. http://doi.org/10.1108/IJRDM-12-2016-0239.
[5] M. Kantardzic, DATA MINING: Concepts, Models, Methods and Algorithms, second ed., New Jersey: John Wiley and Sons, 2011. https://doi.org/10.1002/9781118029145.
[6] G, Liu, Y. Fu, G. Chen, H. Xiong and C. Chen, “Modeling buying motives for personalized product bundle recommendation”, ACM Transactions on Knowledge Discovery from Data, Vol 11(3), 2017, http://doi.org/10.1145/3022185.
[7] J. Ding and S. S.T. Yau, “TCOM, an innovative data structure for mining association rules among infrequent items”, Computers and Mathematics with Applications, Vol 57 (2), 2009, pp. 290-301. http://doi.org/10.1016/j.camwa.2008.09.044.
[8] U. Yun and D. Kim, “Mining of high average-utility itemsets using novel list structure and pruning strategy”, Future Generation Computer Systems, Vol 68, 2017, pp. 346-360. http://doi.org/10.1016/j.future.2016.10.027.
[9] J. Lismont, S. Ram, J. Vanthienen, W. Lemahieu and B. Baesens, “Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation”, Expert Systems with Applications, Vol 104, 2018, pp. 22-32. http://doi.org/10.1016/j.eswa.2018.03.016.
[10] U. Yun and D. Kim, Mining of high average-utility itemsets using novel list structure and pruning strategy, Future Generation Computer Systems, Vol 68, 2017, pp. 346-360. http://doi.org/10.1016/j.future.2016.10.027.
[11] R. Gunawan, E. Winarko and R. Pulungan, A BPSO-based method for high-utility itemset mining without minimum utility threshold, Knowledge-Based Systems, Vol 190, 2020. http://doi.org/10.1016/j.knosys.2019.105164.
[12] S. Krishnamoorthy, Efficiently mining high utility itemsets with negative unit profits, Knowledge-Based Systems, Vol 145, 2018, pp. 1-14. http://doi.org/10.1016/j.knosys.2017.12.035.
[13] L. Zhang, G. Fu, F. Cheng, J. Qiu and Y. Su, A multi-objective evolutionary approach for mining frequent and high utility itemsets, Applied Soft Computing Journal, Vol 62, 2018, pp. 974-986. http://doi.org/10.1016/j.asoc.2017.09.033.
[14] L. Zhou and S. Yau, “Efficient association rule mining among both frequent and infrequent items”, Computers and Mathematics with Applications, Vol 54 (6), 2007, pp. 737-749. http://doi.org/10.1016/j.camwa.2007.02.010.
[15] X. Dong and C. Liu, “Mining interesting infrequent and frequent itemsets based on multiple level minimum supports and minimum correlation strength”, International Journal of Services, Technology and Management, Vol 21, 2015, pp. 301-317. http://doi.org/10.1504/IJSTM.2015.073941.
[16] M. Man, W.A.W. Abu Bakar, M.M. Abd. Jalil and J.A. Jusoh, “Postdiffset algorithm in rare pattern: An implementation via benchmark case study”, International Journal of Electrical and Computer Engineering, Vol 8 (6), 2018, pp. 4477-4485. http://doi.org/10.11591/ijece.v8i6.pp.4477-4485.
[17] B. Bakariya and G. Thakur, “An efficient algorithm for extracting infrequent itemsets from weblog”, International Arab Journal of Information Technology, Vol 16 (2), 2019, pp. 275-280. ISSN: 16833198.
[18] P.Y. Hsu, C.W. Huang, S.H. Huang, P.C. Chen and M.S. Cheng, A novel model for finding critical products with transaction logs, Lecture Notes in Computer Science, Vol 10942, 2018, pp. 432-439. http://doi.org/10.1007/978-3-319-93818-9_41.
[19] P.-Y. Hsu and C.-W. Huang, IECT: A methodology for identifying critical products using purchase transactions, Applied Soft Computing Journal, Vol 94, 2020. http://doi.org/10.1016/j.asoc.2020.106420.
[20] R. Agrawal, T. Imieliński and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, ACM SIGMOD Record, 22 (2), 1993, pp. 207-216. http://doi.org/10.1145/170036.170072.
[21] P. Fournier-Viger, J.C.-W. Lin, B. Vo, T.T. Chi, J. Zhang and H.B. Le, “A survey of itemset mining”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol 7 (4), 2017. http://doi.org/10.1002/widm.1207.
[22] Z.-H. Deng, “DiffNodesets: An efficient structure for fast mining frequent itemsets”, Applied Soft Computing Journal, Vol 41, 2016, pp. 214-223. http://doi.org/10.1016/j.asoc.2016.01.010.
[23] L. Zhang, G. Fu, F. Cheng, J. Qiu and Y. Su, “A multi-objective evolutionary approach for mining frequent and high utility itemsets”, Applied Soft Computing Journal, Vol 62, 2018, pp. 974-986. http://doi.org/10.1016/j.asoc.2017.09.033.
[24] M.J. Zaki and W. Meira. Jr, Data mining and analysis: fundamental concepts and algorithms, New York: Cambridge University Press, 2014.
[25] C. Zhang, P. Tian, X. Zhang, Z.L. Jiang, L. Yao and X. Wang, “Fast eclat algorithms based on minwise hashing for large scale transactions”, IEEE Internet of Things Journal, Vol 6 (2), 2019, pp. 3948-3961. http://doi.org/10.1109/JIOT.2018.2885851.
[26] M.J. Zaki, S. Parthasarathy, M. Ogihara and W. Li, “Parallel algorithms for discovery of association rules”, Data Mining and Knowledge Discovery, Vol 1 (4), 1997, pp. 343-373. http://doi.org/10.1023/A:1009773317876.
[27] M.J. Zaki, “Scalable algorithms for association mining”, IEEE Transactions on Knowledge and Data Engineering, Vol 12 (3), 2000, pp. 372-390. http://doi.org/10.1109/69.846291.
[28] Q. Gao, F.-L. Zhang and R.-J. Wang, “Mining frequent sets using fuzzy multiple-level association rules”, Journal of Electronic Science and Technology, Vol 16 (2), 2018, pp. 145-152. http://doi.org/10.11989/JEST.1674-862X.60408013.
[29] J.C.-W. Lin, L. Yang, P. Fournier-Viger and T.-P. Hong, Mining of skyline patterns by considering both frequent and utility constraints, Engineering Applications of Artificial Intelligence, Vol 77, 2019, pp. 229-238. http://doi.org/10.1016/j.engappai.2018.10.010.
[30] S. Krishnamoorthy, Efficient mining of high utility itemsets with multiple minimum utility thresholds, Engineering Applications of Artificial Intelligence, Vol 69, 2018, pp. 112-126. http://doi.org/10.1016/j.engappai.2017.12.012.
[31] A. Bai, P.S. Deshpande and M. Dhabu, Selective Database Projections Based Approach for Mining High-Utility Itemsets, IEEE Access, Vol 6, 2018, pp. 14389-14409. http://doi.org/10.1109/ACCESS.2017.2788083.
[32] J.M.-T. Wu, J.C.-W. Lin, M. Pirouz and P. Fournier-Viger, TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns, IEEE Access, Vol 6, 2018, pp. 18655-18669. http://doi.org/10.1109/ACCESS.2018.2820740.
[33] C.-H. Weng, Discovering highly expected utility itemsets for revenue prediction, Knowledge-Based Systems, Vol 104, 2016, pp. 39-51. http://doi.org/10.1016/j.knosys.2016.04.009.
[34] V.S. Tseng, C.-W. Wu, P. Fournier-Viger and P.S. Yu, Efficient algorithms for mining the concise and lossless representation of high utility itemsets, IEEE Transactions on Knowledge and Data Engineering, Vol 27 (3), 2015, pp. 726-739. http://doi.org/10.1109/TKDE.2014.2345377.
[35] K. Black, Business Statistics for Contemporary Decision Making, sixth ed., US: John Wiley and Sons, 2010.
[36] M. Song, X. Zhao, H. E and Z. Ou, “Statistics-based CRM approach via time series segmenting RFM on large scale data”, Knowledge-Based Systems, Vol 132, 2017, pp. 21-29. http://doi.org/10.1016/j.knosys.2017.05.027.
[37] P.A. Sarvari, A. Ustundag and H. Takci, “Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis”, Kybernetes, Vol 45 (7), 2016, pp. 1129-1157. http://doi.org/10.1108/K-07-2015-0180.
[38] A. Dursun and M. Caber, “Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis”, Tourism Management Perspectives, Vol 18, 2016, pp. 153-160. http://doi.org/10.1016/j.tmp.2016.03.001.
[39] E. Nikumanesh and A. Albadvi, “Customer′s life-time value using the RFM model in the banking industry: A case study”, International Journal of Electronic Customer Relationship Management, Vol 8, 2014, pp. 15-30. http://doi.org/10.1504/IJECRM.2014.066876.
[40] S. Khandelwal and A. Mathias, “Using a 360° view of customers for segmentation”, Journal of Medical Marketing, Vol 11 (3), 2011, pp. 215-220. http://doi.org/10.1177/1745790411408853
[41] T. Tanaka, T. Hamaguchi, T. Saigo and K. Tsuda, “Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores”, Procedia Computer Science, Vol 112, 2017, pp. 956-964. http://doi.org/10.1016/j.procs.2017.08.133.
[42] S. Peker, A. Kocyigit and P.E. Eren, “LRFMP model for customer segmentation in the grocery retail industry: a case study”, Marketing Intelligence and Planning, Vol 35 (4), 2017, pp. 544-559. http://doi.org/10.1108/MIP-11-2016-0210.
[43] M. Namvar, S. Khakabimamaghani and M.R. Gholamian, “An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features”, International Journal of Electronic Customer Relationship Management, Vol 5, 2011, pp. 220-235. http://doi.org/10.1504/IJECRM.2011.044688.
[44] A. Hiziroglu, “Soft computing applications in customer segmentation: State-of-art review and critique”, Expert Systems with Applications, Vol 40 (16), 2013, pp. 6491-6507. http://doi.org/10.1016/j.eswa.2013.05.052.
[45] P. Govender and V. Sivakumar, “Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019)”, Atmospheric Pollution Research, Vol 11 (1), 2020, pp. 40-56. http://doi.org/10.1016/j.apr.2019.09.009.
[46] A.K. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, Vol 31 (8), 2010, pp. 651-666. http://doi.org/10.1016/j.patrec.2009.09.011.
[47] D.C.S. Beddows, M. Dall′Osto and R.M. Harrison, “Cluster analysis of rural, urban, and curbside atmospheric particle size data”, Environmental Science and Technology, Vol 43 (13), 2009, pp. 4694-4700. http://doi.org/10.1021/es803121t.
[48] P.-N. Tan, M. Steinbach, A. Karpatne and V. Kumar, Introduction to Data Mining, second ed., New York: Pearson Education, 2019.
[49] Y. Su, J. Reedy and R.J. Carroll, “Clustering in general measurement error models”, Statistica Sinica, Vol 28 (4), 2018, pp. 2337-2351. http://doi.org/10.5705/ss.202017.0093.
[50] L. de la Fuente-Tomas, B. Arranz, G. Safont, P. Sierra, M. Sanchez-Autet, A. Garcia-Blanco and M.P. Garcia-Portilla, “Classification of patients with bipolar disorder using k-means clustering”, PLoS ONE, Vol 14 (1), 2019. http://doi.org/10.1371/journal.pone.0210314.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2021-1-25

推文